With the introduction of advanced digital delivery systems for audio and video, there is an increased awareness of the relationship between subjective (perceived) quality and objective (measured) quality of video images presented to the observer's eye. Video quality is a characteristic of a video passed through a video transmission/processing system, a formal or informal measure of perceived video degradation (typically, compared to the original video). Video processing systems may introduce some noticeable amounts of distortion or artifacts in the video signal, so video quality evaluation is an important problem.
Currently there are many tools for analyzing video quality utilizing the Full Reference Methods (FR) such as dual-stimulus methodology based on calculation of differences between original and processed video data and subsequent transformation of these differences in accordance with predetermined metrics.
Typically, objective methods are often classified based on the availability of the original video signal, which is considered to be of high quality (generally not compressed). These metrics are usually used when the video coding method is known. PSNR (Peak Signal-to-Noise Ratio) is the most widely used objective video quality metric. However, PSNR values do not perfectly correlate with a perceived visual quality due to non-linear behavior of human visual system. The PSNR calculation on the pre-selected set of live clips is very long and tedious job, so in fact it is executed only during acceptance test of some large-scale systems. In other words, this methodology is not suitable for fast measurement of large quantity of different video processors and/or processing modes/profiles. More sophisticated metrics require even more calculations, thus they are even less suitable for fast objective measurements.
Moreover, PSNR compression artifacts metering implies that both A and B picture have same resolution, horizontal and vertical positions, video levels and (very important)—same frequency response, i.e. both pictures are perfectly aligned in space and time. Only under these conditions PSNR reading correlates well with subjective quality estimates. In modern content delivery systems such conditions are very seldom satisfied.
A second approach is represented by well established techniques of measuring objective video processing parameters on some artificial matrix test pattern. This approach captures video data and subsequently analyzes the captured video data. However, automatic video analyzers in this approach suffer from lack of flexibility: they are limited to a short list of video image resolutions and signal formats—any image size/position/resolution deviation from perfect match results in a failure of the analysis process. Additionally, analysis of pre-captured data files is not supported. With application to the analysis of video cameras performance, analyzers of this kind provide mainly waveform monitor functionality, i.e. only manual controls, thus excluding any automated analysis.
A third approach is represented, for example, by IE-Analyzer made by Image Engineering, Gmbh in Germany. This automated hardware/software tool is suitable for accurate and detailed camera performance analysis, but requires a nearly perfect setup of lighting conditions and camera's pan/zoom/tilt controls. IE-Analyzer can work with pre-captured files, but positioning of dotted lines delimiting the ROI (Region Of Interest) should be done manually. Moreover, for each reported parameter a different reflectance test chart or test pattern transparency is required, so the complete measurement process takes a long time, and nearly perfect studio conditions and highly skilled technical personnel are pre-requisites.